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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/7598
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dc.contributor.authorSantra, Ion-
dc.contributor.authorBasu, Urna-
dc.contributor.authorSabhapandit, Sanjib-
dc.date.accessioned2020-12-14T05:44:18Z-
dc.date.available2020-12-14T05:44:18Z-
dc.date.issued2020-11-
dc.identifier.citationJournal of Statistical Mechanics:Theory and Experiment, 2020, Article No.113206en_US
dc.identifier.issn1742-5468-
dc.identifier.urihttp://hdl.handle.net/2289/7598-
dc.descriptionRestricted Accessen_US
dc.description.abstractWe study the effect of stochastic resetting on a run and tumble particle (RTP) in two spatial dimensions. We consider a resetting protocol which affects both the position and orientation of the RTP: with a constant rate the particle undergoes a positional resetting to a fixed point in space and orientation randomization. We compute the radial and x -marginal stationary state distributions and show that while the former approaches a constant value as r→0 , the latter diverges logarithmically as x→0 . On the other hand, both the marginal distributions decay exponentially with the same exponent far away from the origin. We also study the temporal relaxation of the RTP and show that the position distribution undergoes a dynamical transition to a stationary state. We also study the first passage properties of the RTP in the presence of the resetting and show that the optimization of the resetting rate can minimize the mean first passage time. We also give a brief discussion on the stationary states for resetting to the initial position with fixed orientation.en_US
dc.language.isoenen_US
dc.publisherIOP Publishing and SISSAen_US
dc.relation.urihttps://ui.adsabs.harvard.edu/abs/2020arXiv200909891S/abstracten_US
dc.relation.urihttps://arxiv.org/abs/2009.09891en_US
dc.relation.urihttps://doi.org/10.1088/1742-5468/abc7b7en_US
dc.rights2020, IOP Publishing and SISSAen_US
dc.subjectactive matteren_US
dc.subjectexact resultsen_US
dc.subjectpersistenceen_US
dc.titleRun-and-tumble particles in two dimensions under stochastic resetting conditionsen_US
dc.typeArticleen_US
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